🇬🇧 AI for Learning Analytics and Predictive Insights

Omschrijving

In this session, we will look at the goldmine of data already sitting inside your Moodle site and discover how to turn it into a crystal ball for student success. While many institutions use “Learning Analytics” to look at what has already happened, we will explore how Predictive AI can identify at-risk students weeks before they fall behind.

We will walk through a real-world framework for building a predictive model—from selecting the right data points (participation pattern, online assessment, and submission patterns) to the ethical implementation of automated interventions.

Participants will leave with a clear roadmap on how to move from “passive reporting” to “proactive teaching” without needing a PhD in Data Science. This is not about a specific tool; it’s about a new strategy for student retention in the age of AI.

Voor wie?

This presentation is designed for a cross-functional audience:

Teachers & Instructors: Who want to know which students need help now rather than after the final exam.
Managers & Decision Makers: Who are looking for data-driven strategies to improve institutional retention rates and student satisfaction.
Admins & Techies: Who want to understand the underlying data architecture and ethical considerations (privacy/bias) required to make AI work in Moodle.

Wat kun je verwachten?

Interactive Case-Study Session (40 Minutes) To ensure maximum engagement and avoid “death by PowerPoint,” this session will be structured as follows:

The “Identify the Risk” Challenge (10 mins): I will present anonymized data patterns and ask the audience to “predict” which student is at risk, highlighting the limitations of human observation vs. AI.
The Strategic Framework (20 mins): A deep dive into the logic of predictive analytics, focusing on the “Early Warning System” workflow.
Live Q&A & Ethical Debate (10 mins): A structured discussion on the ethics of AI predictions—how do we ensure we aren’t “labeling” students unfairly?